用时频分析法研究铝合金电阻点焊过程中的声音信号

研究了铝合金点焊过程中声音信号的时频域能量分布，并应用它来识别点焊的熔核状态。点焊过程中的声音信号包含着熔核形成信息，是点焊质量监控的重要参数。为了能够确定熔核的变化状态，作者尝试采用非平稳过程的时频分析方法对声音信号进行研究，试验与计算结果表明，Choi-williams时频分布是一种较好的铝合金点焊声音分析方法，可以区分不同熔核状态，其计算结果可作为铝合金点焊过程的监控参数和质量检验的依据，具有一定的实用价值，同时它对其它焊接方法中的信号分析与处理也有较高的参考价值。     

基于LabVIEW的铝合金点焊数据采集系统的设计

介绍了一种铝合金点焊过程电压、电流、声音和电极位移信号的实时采集系统,并给出了该采集系统所采用的传感器和数据采集卡等硬件构成原理和使用虚拟仪器设计软件LabVIEW编制的程序框图.试验表明：该采集系统能够正确采集铝合金点焊过程的各个特征信号,并能有效反映焊接过程中焊点熔核的变化,可以满足点焊过程实时监测的要求.     

基于LabVIEW铝合金点焊电流和电极压力监控系统的研究

在监控铝合金电阻点焊焊接过程中,还没有一种传感器可以准确可靠地检测出熔核的成形质量.在点焊过程中压力信号被认为能够提供关于熔核成形最多的信息.利用LabVIEW语言开发了点焊监控系统,对电流信号及电极压力信号进行实时采集,可以有效地检测点焊熔核质量的变化.     

MEMS压力传感器在电阻点焊压力测试中的应用

针对目前航天焊接生产中压力信号测量问题,采用MEMS压力传感器代替原有的压电式压力传感器采集点焊电极压力信号.实验证明,与常用的压电式压力传感器相比,根据点焊电极压力特点专门制造的MEMS微机械压力传感器排除了点焊机械结构的影响,分辨率更高,工作范围更大,有利于点焊缺陷的判别;可以获取完整的压力信号,从而预防点焊缺陷的产生.     

用Lyapunov指数研究点焊位移信号的混沌特性

文中以混沌理论为基础,阐述了Lyapunov指数作为点焊位移时间序列的混沌判断原理,并对电阻点焊位移信号中进行了分析与研究,计算了4种不同工作状态下点焊位移信号时间序列的Lyapunov指数,研究表明,点焊位移信号中的最大Lyapunov指数均大于0,根据混沌理论可以断定点焊位移信号中存在混沌现象,应该采用混沌的方法研究与分析点焊位移信号,该研究为点焊质量的判断和预测开辟了新的途径.     

独立分量分析（ICA）在齿轮箱故障诊断系统中的应用

文章应用基于互信息最小化的独立分量分析算法(ICA算法),提出了一种用于齿轮箱故障诊断的信号预处理方法FASTICA,并应用确定性混合信号进行仿真验证.以JZQ200型号齿轮箱为例,应用FASTICA对采集的振动信号进行预处理,处理结果表明,经ICA分离后故障信息明显增强,故障诊断精度可以明显提高.     

薄镀锌钢板点焊超声谐振检测

研究了低频超声探头的谐振特性,并将这种谐振特性用于镀锌薄板的点焊检测中.采用接触式低频探头对点焊进行检测时,检测探头能够接收到未焊薄基板产生的半波谐振高频信号,这些半波谐振信号的高频部分处于探头频带外的奇数次谐振频率附近.通过小波包变换的方法可以将这些高频信号从检测信号中分离出来并用于点焊的质量评价.分析小波包变换中各尺度信号,获取了能够评价焊核质量的超声波特征值并将这些特征值与点焊力学性能联系起来,提供了一种能够定量评价点焊质量的超声无损评价方法.此外,采用低频探头对点焊进行检测较之普通检测方法将大大降低检测成本.     

基于独立分量分析的罐底腐蚀声发射信号去噪方法

针对储油罐罐底腐蚀声发射信号易受多种噪声干扰,影响定位精度和评判准确性的问题,在对干扰噪声进行分析的基础上,提出了一种基于独立分量分析的信号去噪方法。首先利用混合因子分析进行预处理,再利用改进的独立分量分析算法实现源信号的分离,根据时域和频域的相关知识,将独立分量中的噪声通道去除,从而达到声发射信号有效去噪的目的。试验表明,独立分量分析较常用的去噪方法能得到更高的信噪比,去噪后的信号定位精度更高,从而证明该预处理方法和改进的独立分量分析算法的有效性。     

铝合金点焊电极位移时序相空间重构及混沌特征分析

以混沌理论和相空间重构方法为基础对点焊焊接过程中电极位移信号时间序列进行了特征分析,并分别对试验获得的多组点焊位移信号时间序列的最佳时间延迟τ、关联维数D、嵌入维数m及最能体现混沌特征的李雅普诺夫(Lyapunov)指数进行了定量化的计算.分析发现点焊焊接过程中电极位移信号在某些情况下具有混沌特征,并与目前采用的小波能谱分析、分形维数分析进行了比较.结果表明,信号的Lyapunov指数特征能够更加精确的反映熔核尺寸变化.     

基于支持向量机的点焊质量分类

以点焊质量监控为目的,通过对点焊过程电极位移信号、动态电阻信号的同步采集和信号分析,从2种信号中提取12个有效的特征分量,构造点焊过程的监测数据集,以焊点的抗剪强度为指标,利用支持向量机(SVM)和RBF神经网络对焊点质量进行分类.测试结果表明,2种信号特征向量作为SVM的输入向量,在参数匹配的情况下,使用不同的核函数对喷溅和焊点质量分类均能取得较好的分类结果,分类器性能明显优于RBF神经网络模型.小样本情况下SVM具有较好的泛化能力,正确率仍保持在80%以上,据此认为提取电极位移和动态电阻的特征向量构筑输入向量,建立的SVM动态模型对点焊接头进行质量分类是可行的.     

Chaos analysis of acoustic emission signals in spot welding process

Bused on chaos time series and fiactal theory, acoustic emission signals were studied in the process of spot welding. According to calculating 8 welding parameters using phase space reconstruction method, the largest Lyapunov exponents were positive values and chaos characteristics were firstly discovered from acoustic emission signals in spot welding. In order to evaluate acoustic emission signal, Hausdorff dimension is put forward to analyze and estimate chaos characteristics. The experiment and calculation results indicate that the Hausdorff dimension of acoustic emission signal is significantly distinguishable in the nuggets with different welding parameters. This research provides a new method for measuring the resistance spot welding quality.     

Identification of acoustic emission signal in aluminum alloys spot welding based on fractal theory

The acoustic emission signal of aluminum alloys spot welding includes the information of forming nugget and is one of the important parameters in the quality control. Due to the nonlinearity of the signals, classic Euclidean geometry can not be applied to depict exactly. The fractal theory is implemented to quantitatively describe the characteristics of the acoustic emission signals. The experiment and calculation results show that the box counting dimension of acoustic emission signal, between 1 and 2, are distinctive from different nugget areas in AC spot welding. It is proved that box counting dimension is an effective characteristic parameter to evaluate spot welding quality. In addition, fractal theory can also be applied in other spot welding parameters, such as voltage, current, electrode force and so on, for the purpose of recognizing the spot welding quality.     

电阻点焊熔核形核信息的声发射信号表征

以结构负载压电传感的方式,研究借助声发射信号表征电阻点焊铝合金过程的熔核形核信息,包括焊接过程的声发射信号表征和熔核质量信息的声发射信号表征.结果表明,电阻点焊不论是稳定形核还是非稳定形核均可以用声发射信号来表征熔核形核的不同物理阶段.熔核形核的飞溅、开裂等特征事件也可以利用电阻点焊过程声发射信号的形核声发射特征参数来予以界定.声发射特征参数与熔核形核尺寸、焊点拉剪载荷具有较好的相关性.可以使用焊接过程中的声发射特征参数作为检测或预测熔核形核尺寸和焊点拉剪载荷的依据之一.     

激光-微束等离子弧复合焊接过程的结构负载声发射信号表征

采用脉冲YAG激光焊、微束等离子弧焊和激光-微束等离子弧复合焊对304不锈钢进行焊接试验,实时采集焊接过程中的结构负载声发射信号,研究声发射信号对焊接过程的表征. 结果表明,激光离焦量为-2 mm的激光-微束等离子弧复合焊接,热源复合效果更好,焊缝表现出更显著的复合热源焊接特征. 焊缝获得了更好的表面成形和熔深、熔宽. 热源复合效果更好的焊接过程,其声发射信号波形表现出更大振幅,且更为均匀,并与激光热源的周期性相符,呈现出明显的复合热源焊接周期性. 因此可以利用焊接过程检测到的结构负载声发射信号表征激光和微束等离子弧两种热源复合效果特征.     

基于结构负载声发射信号检测的镀锌钢板电阻点焊飞溅主控因素分析

利用实时监测结构负载声发射信号实现了镀锌钢板电阻点焊过程中的飞溅现象的检测. 以焊接飞溅声发射事件的正峰值和振铃计数为考核指标,通过正交试验设计法设计焊接试验和方差分析法进行显著性检验,研究了镀锌钢板电阻点焊飞溅的主控因素. 结果表明,焊接飞溅释放出的能量当量可由其声发射事件的正峰值和振铃计数两个特征参数定量评估. 影响焊接飞溅的主要因素为焊接电流和电极力,而焊接电流所起的作用比电极力更为重要. 焊接时间和焊接电流与电极力的交互作用对焊接飞溅的影响相差不大. 焊接电流和焊接时间越大,焊接飞溅当量越大;电极力越大,焊接飞溅当量越小.     

Formation of hardened layers on aluminium alloy surfaces by the plasma transferred arc process

A new criterion for stability analysis of the gas metal arc welding (GMAW) process is proposed and presented in this work, based on acoustic emission generated by the arc during short-circuiting metal transfer. For the experimental development an AWS ER70S-6 wire with a diameter of 0.8 mm and a DEP 401 rectifier were used. The weld bead was carried out on a 4-mm-thick AISI 1020 steel plate. Several welding conditions were studied with variation of the process parameters during the deposition of the beads. The acoustic emission signals were acquired using a measurement system composed of a MV-201 microphone, with a sensitivity of 10 ± 3 mV Pa^? 1 and frequency bandwidth of 20 Hz to 170 dB to 100 kHz, and a data acquisition card coupled to a PC. A stability index was proposed. Eventually, a statistical analysis for validation of the obtained experimental results was carried out. The outputs allowed obtained a relationship between the acoustic signals and the arc voltage signals. The feasibility of the proposed index, and the effectiveness of the method as a novel means of analysing the stability of arc welding, was demonstrated based on acoustic emission for analyses of GMAW process stability.     

Process monitoring to assist the workpiece surface quality in machining

The paper reports on research which attempts to correlate the quality of the machined surface after broaching and the output signals obtained from multiple sensors, namely acoustic emission, vibration, and cutting forces. The quality of the machined surface was estimated in terms of geometrical accuracy, burr formation, chatter marks and surface anomalies. Cutting conditions were varied based on an orthogonal array with cutting speed, coolant conditions, and tool settings as factors. Each orthogonal array was repeated at three levels of the tool wear.The results show that the cutting force signals are sensitive enough to detect the geometrical deviation of the machined profile, burr formation and to a lesser extent the chatter marks. The vibration signals were found sensitive to detect the chatter marks while the acoustic emission signal proved to be efficient for the detection of small surface anomalies such as pluckings, laps, and smeared material. However, up to now, no clear distinction between the different types of the surface anomalies could be made using the analysis of the acoustic emission signal.Time and frequency domain analysis of the output signals were carried out in order to develop appropriate techniques for qualitative/quantitative evaluation of the machined surface quality.It was found that each sensory signal is rather limited to a narrow field of application where certain surface features are detectable. The limitations of the employed sensory signals/analysis methodologies used to assess the workpiece surface quality, and their applicability in the industrial machining conditions are also discussed.     

基于独立元分析的GMAW缺陷在线监测

采用独立元分析(ICA),用于气体保护焊过程在线监测.通过对过程的电参数进行独立元分析,去除焊接电流和电弧电压之间的相关性并提取出独立元.通过试验验证ICA对GMAW过程在线监测的可行性,发现当焊接过程有突变性干扰时,独立元(IC)能够对干扰做出反应.结果表明,IC可以作为表征过程是否异常的一个状态变量,通过对IC进行统计过程控制,能够分离出焊穿缺陷.并将ICA与主元分析(PCA)的结果进行了比较.     

A magnetic sensor array for spot welding quality monitor

Spot welding is affected by many factors and is difficult to monitor the nugget information with single sensor. In this paper, a new monitor system based on sensors array technique was developed for spot welding quality detection. The key part in the system is cross magnetic sensor array. It is composed of six magnetic sensors. An improved algorithm was proposed to extract the signal eigenvalue, which was based on principle component analysis. The results show that in the case of the 60 mm experiment, cross magnetic sensor array works well. And when the eigenvalues range of the cross magnetic sensor array is 0.090 1-0.098 2, the spots quality is good. The analysis of the eigenvalues of the cross magnetic sensor array allows us to determine whether the spot quality is good or bad.